Benefits of Aviation Weather Services: A Review of International Literature
Bibliographic record
Abstract
This paper presents a summary of the international literature published on the benefits of aviation weather services. Aviation operations are highly sensitive to weather conditions. Information on weather conditions helps meteorologists, pilots, navigators, airline companies and businesses to ensure safe flights and save money by reducing some of the stringent requirements related to carrying extra fuel loads. The development of constantly updated flight plans with respect to available weather information regarding changing wind and general weather conditions can enable aircraft to use fuel more efficiently and navigate their planes in safer environments that avoid turbulence and make air flights comfortable to the travelling public. The summary literature presented in this paper illustrates the importance of the work of meteorologists in the production of relevant information and data that are accessible to pilots and navigators. The pooling of meteorological information, data and other resources by member countries of the World Meteorological Organisation represents a classic case of international cooperation that has ensured relatively safe and comfortable air flights across the world since the era of international air travel in the 20th Century speeding up the process of the more historically-recent globalisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".